Predictive customer service environment

ABSTRACT

A mechanism for facilitating customer interactions within a customer service environment provides prompt and accurate answers to customer questions. A smart chat facility for use in a customer service environment to predict a customer problem examines a customer chat transcript to identify customer statements that set forth a customer issue and, responsive to this, can route the customer to an agent, an appropriate FAQ, or can implement a problem specific widget in the customer UI. Customer queries are matched with most correct responses and accumulated knowledge is used to predict a best response to future customer queries. The iterative system thus learns from each customer interaction and can adapt to customer responses over time to improve the accuracy of problem prediction.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/926,988, filed Jun. 25, 2013, which is a divisional application ofU.S. patent application Ser. No. 13/239,195, filed Sep. 21, 2011, andclaims priority to U.S. provisional patent application Ser. No.61/385,866, filed Sep. 23, 2010, and each of which application isincorporated herein in its entirety by this reference thereto.

BACKGROUND OF THE INVENTION Technical Field

The invention relates to customer/agent interaction. More particularly,the invention relates to problem prediction and context based resolutionin a customer service environment.

Description of the Background Art

The customer experience has never been more important, informed bytechnological advancements such as Google Predictive Search, and thecollective power of social media to influence consumer sentiment andbehavior. Executive responsible for customer care face the challenge ofmeeting rising expectations without increasing costs.

Strong growth in the variety and utilization of connected devices—fromPCs to smart phones and tablets—has made the online channel the oneconsumers love to use. It is an opportunity for companies to redefineand dramatically improve customer service and manage costs—if they canpredict customer needs and address them quickly and courteously—thefirst time, every time.

For example, the quality of service provided to a customer directlyaffects the relationship between the customer and a merchant. Callcenters and Web-based customer service facilities strive to provideprompt and helpful answers to customer questions, but often fail to doso. The process of getting an answer to a question can be bothtime-consuming and frustrating for a customer, as well as for the agentwho is assisting the customer.

It would be advantageous to provide a mechanism for facilitatingcustomer interactions within a customer service environment to provideprompt and accurate answers to customer questions.

SUMMARY OF THE INVENTION

An embodiment of the invention provides predictive experience solutionsfor online customer experiences. Unlike proactive chat and reactiveweb-self-service, an embodiment of the invention predicts not only whoneeds assistance, but when, where, why, and how. The invention alsointegrates self-service and chat in a compelling interface that guidescustomer journeys across multiple pages and queues, if necessary.

One embodiment of the invention provides a mechanism for facilitatingcustomer interactions within a customer service environment to provideprompt and accurate answers to customer questions. In particular, thisembodiment of the invention provides a smart chat facility for use in acustomer service environment to predict a customer problem. For example,the invention examines a customer chat transcript to identify customerstatements that set forth a customer issue and, responsive to this, canroute the customer to an agent, an appropriate FAQ, or can implement aproblem specific widget in the customer UI. The invention provides afacility that learns to match customer queries with most correctresponses and then uses this accumulated knowledge to predict a bestresponse to future customer queries. Because the invention provides aniterative system that learns from each customer interaction, the systemcan adapt to customer responses over time and improve the accuracy ofproblem prediction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block schematic diagram showing a prediction servicesplatform architecture according to the invention;

FIG. 2 is a block schematic diagram showing prediction services platformclient integration according to the invention;

FIG. 3 is a block schematic diagram that shows high level state flow,including variable and transitions according to the invention;

FIG. 4 is a block schematic diagram that shows how event handlerstrigger actions or state changes, or present widgets to engage with thecustomer according to the invention;

FIG. 5 is a block schematic diagram that shows data and actions inconnection with a customer Web journey according to the invention;

FIG. 6 is a block schematic diagram showing an architecture for a smartchat client for problem prediction in a customer service environmentaccording to the invention;

FIG. 7 is a block schematic diagram that shows a problem predictor inwhich chat is used to bootstrap guided self-service according to theinvention;

FIG. 8 is a flow diagram that provides an example of the use of issueprediction to trigger an appropriate guided self-service widgetaccording to the invention;

FIG. 9 is a flow diagram that shows an example of primary issue lineidentification according to the invention;

FIG. 10 is a flow diagram that shows a further example of primary issueline identification according to the invention;

FIG. 11 is a flow diagram showing an average hold time (AHT) reductionalgorithm according to the invention;

FIG. 12 is a flow diagram that shows an example of a text miningapproach according to the invention;

FIGS. 13a and 13b are a flow diagrams showing an algorithm forextraction of a primary question during a chat session (FIG. 13a ) andextracting an issue (FIG. 13b ) according to the invention;

FIG. 14 shows a sample progress table for a problem predictor accordingto the invention;

FIG. 15 is a flow diagram that shows a PSP process in the datacollection and rule evaluation stages according to the invention;

FIG. 16 is a timeline that shows a visitor session life cycle accordingto the invention;

FIG. 17 is a block schematic diagram that shows periodic, on-demand useractivity information tracking according to the invention;

FIG. 18 is a block schematic diagram that shows the multiple tab windowsynchronization problem;

FIG. 19 is a block schematic diagram that shows a client sideinformation storage system according to the invention;

FIG. 20 is a flow diagram that shows a multiple tab synchronizationalgorithm according to the invention;

FIG. 21 is a block schematic diagram that shows a high level design of areal time experimentation framework according to the invention;

FIG. 22 is a flow diagram that show visitor tagging according to theinvention;

FIG. 23 is a flow diagram that shows real time experimentable valuedetermination according to the invention; and

FIG. 24 is a block schematic diagram of a machine in the exemplary formof a computer system within which a set of instructions for causing themachine to perform any one of the herein disclosed methodologies may beexecuted.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the invention provides a prediction platform forcustomer service that analyzes Web behavior, customer data, call data,and individual journeys of Website visitors to help segmentend-customers with a high propensity to call. It then eliminates theneed for the call by providing a highly personalized and uniquepredictive chat or self serve interaction, thereby eliminating potentialcalls from the Website.

FIG. 1 is a block schematic diagram showing a prediction servicesplatform architecture according to the invention. Broadly, the platformincludes a client side element 21 that comprises one or more clients 20,a server side element 22, and a reporting element 23.

FIG. 2 is a block schematic diagram showing prediction services platformclient integration according to the invention. Client integration pointsare shown within the client 20 and include specifically the actionhandlers and event manager. Within the server 22, client integrationpoints include the client data integrator. The client's server/Websiteincludes pages 1-n, the client integration libraries, and the clientdata repository. The framework components include the chat platformintegration libraries, session manager, persistent storage, rulesengine, FSM state manager, controller, widget libraries, widget runtimeand server 22.

Another embodiment of the invention provides a mechanism forfacilitating customer interactions within a customer service environmentto provide prompt and accurate answers to customer questions. Inparticular, the invention provides a smart chat facility for use in acustomer service environment to predict a customer problem. For example,the invention examines a customer chat transcript to identify customerstatements that set forth a customer issue and, responsive to this, canroute the customer to an agent, an appropriate FAQ, or can implement aproblem specific widget in the customer UI. The invention provides afacility that learns to match customer queries with most correctresponses and then uses this accumulated knowledge to predict a bestresponse to future customer queries. Because the invention provides aniterative system that learns from each customer interaction, the systemcan adapt to customer responses over time and improve the accuracy ofproblem prediction.

Predictive Service Platform

One embodiment of the invention provides a predictive service platformthat models a user's Web journey. A finite state machine is implementedconsisting of different states and transitions between the states.

FIG. 3 is a block schematic diagram that shows high level state flow,including variable and transitions according to the invention. FIG. 3includes a primary FSM, a sub flow FSM and a sub flow, and a feedbackflow FSM and a feedback flow.

The predictive service platform provides real time interaction solutionsin conjunction with a Website, where the predictive service platform isintegrated to the Website, for example, via java scripting. Anembodiment provides an event driven customizable action executionmechanism to facilitate contextual interactions, and also provides ageneric session management and information tracking mechanism to capturea user's Web journey.

FIG. 4 is a block schematic diagram that shows how event handlerstrigger actions or state changes, or present widgets to engage with thecustomer according to the invention.

The predictive service platform captures the context, i.e. data andactions, of the visitor journey through the site and takes contextualdecisions by use of sophisticated analytical models that focus on how toengage the visitor.

FIG. 5 is a block schematic diagram that shows data and actions inconnection with a customer Web journey according to the invention. Suchdata and actions can pertain to any of the following broad categories:

-   -   Whom to engage: based on the context, especially collected data        elements of the visitor including, but not limited to,        customer/visitor's prior history of interactions, Web journey,        and Web behavior;    -   When and where to engage: based on the context, especially the        journey of the user, time spent on the journey, etc.; and    -   What to show: based on the context, especially according to the        targeted way to engage the customer.        Example Embodiments of the Predictive Service Platform

Key solutions offered on the predictive service platform for the Webmedium primarily can be categorized into the following examplecategories:

Sales solutions, which:

-   -   Identify and engage buyers to increase sales via a rules engine        driven chat interaction;    -   Provide a proactive and reactive way to engage customers via        invitations and contextual content presentation that let the        user initiate a chat reactively; and    -   Increase conversion via customized actions and content,        including multi-media content, e.g. video, audio and chat        enabled sales that show targeted promotions, cross sell, and up        sell.

Service solutions, which:

-   -   Increase self-serve resolution rates through targeted self        service for specific problems, where a problem predictor is used        to identify the right issues of the user and guided resolution        paths are provided to help the user go though an assisted        journey in the main Website to solve a problem; and    -   Improve customer loyalty and satisfaction via contextual and        targeted content and actions and targeted problem prediction and        resolutions.

The predictive service platform thus provides a novel approach forbuilding and provisioning real time interaction management solutionsover the Web, which captures the user's journey, e.g. data, actions, andtime on all pages, and models the same as a finite state machineconsisting of distinct states and conditional transitions between them.In an embodiment, a page load results into a state transition, any eventcan cause a state transition, and all metadata for action invocation isattached to a state.

An embodiment provides an event-driven, customizable action executionmechanism to facilitate contextual interactions, in which: any event cantrigger an action to be taken, different event handlers are allowed tobe configured to take specific actions; an event handler can cause thestate transition; an event handler shows up an interaction popup; a selfservice wizard, or any other customized interaction interface to theuser; an event handler can initiate a chat conversation at any point intime; issues and resolutions can be predicted based on predictivemodels; and multiple mediums of interaction can be employed during aninteraction, e.g. chat, self service, emails, click to call, etc.

An embodiment also provides a generic session management and informationtracking mechanism to capture user's journey, in which sessions can begenerated to identify a visitor, a logical browsing session of the user,as well as a logical interaction with the user and periodical andon-demand update of tracked information can be sent to the server.

The invention takes advantage of the persistent local storage on thebrowser to keep track of all the information throughout the user'sjourney, thereby making the server stateless. Accordingly, informationcan be shared across multiple windows, all contextual information iscaptured and can be sent across in any of the client-serverinteractions, the server is completely stateless, thereby providingmaximum scalability and availability, and different storage options canbe used in the browser, e.g. Flash cookie, local storage, cookies, etc.

Smart Chat

FIG. 6 is a block schematic diagram showing an architecture for a smartchat facility for problem prediction in a customer service environmentaccording to the invention. In FIG. 6, an agent console 10 interactswith a problem predictor 15. In a presently preferred embodiment, aplurality of problems are predicted, where each problem prediction hasrelated articles which provide resolution for each of the predictedproblems with which a customer interacts.

FIG. 7 is a block schematic diagram that shows a problem predictor inwhich chat is used to bootstrap guided self-service according to theinvention.

FIG. 8 is a flow diagram that provides an example of the use of issueprediction to trigger an appropriate guided self-service widgetaccording to the invention.

A feature of the invention provides a smart chat routing module 17 (FIG.6) with which customer chat is routed to service center agents basedupon an issue/agent mapping, as well as an agent performance score. Withregard to the agent performance score, see U.S. patent application Ser.No. 13/161,291, filed Jun. 15, 2011, which application is incorporatedherein in its entirety by this reference thereto.

The agent console operates in coordination with a module that generatesreal time triggers 11, such as agent alerts and supervisor alerts. Thesealerts are based on such factors as, for example, if the agent is notdoing what they are supposed to, e.g. they could be taking too long torespond (discussed below); or they could be taking more time than normalfor any one stage of the chat (discussed below); or they could beresponding to the wrong issue, as determined based on the issueidentification model. These triggers are based upon inputs 13 thatinclude, for example, an off line average hold time (AHT) reductionsolution and values for customer pre-chat time, issue time, andpost-chat time.

The agent console operates in conjunction with a customer console 12 toestablish a smart chat session that is guided by outputs 19 thatinclude, for example, responses to FAQs that are generated based upondynamic text mining, The system selects the right issue/issues beingfaced by the customer, matches them to FAQ responses to those issues andprovides appropriate widgets for resolving these standardissues/questions. For example, if “Understanding Bill Details” is thekey issue the customer is trying to resolve, the primary issue lineidentification algorithm shown in FIGS. 9 and 10 identifies the primaryissue line. This line is text mined to identify the primary issue, inthis case “Understanding Bill Details,” and the widget that helps toexplain the bill details is then deployed for customer interaction.

In FIG. 11, chat related information is divided into pre-chat, actualchat, and post chat (200). Pre-chat, actual chat, and post-chat aredescribed below. Of these, all except pre-chat time add up to make theaverage handle time (AHT).

Pre-chat Time—Customer has accepted a chat invitation, but the chat hasnot started.

Pre-Issue Time—Customer has started chat, but is still in the greetingsand customer statement of the issue.

Authentication Time—Customer gets authenticated by the agent before theagent starts issue resolution.

Issue Resolution Time—Time taken by the agent to resolve the issue.

Closure time—Time taken by the agent to close the chat after the issuehas been resolved.

Wrap-up time—Time taken by the agent to do some after chat work, i.e.filling disposition forms regarding details of the chat and thecustomer.

Similarly, another key parameter that is tracked is average responsetime (ART) for the agent. Average response time is the average timetaken by the agent to respond to a customer sentence. It is measured bythe difference in timestamp between a customer query and an agentresponse. If the ART is high or the response time has a lot of variance,then this is a key driver of customer dissatisfaction

The position in the chat transcript at which an issue is posed by thecustomer is identified (202). The system text mines the chat transcriptfor key lines that identify the issue (204). (See, also, FIG. 12, whichis a flow diagram that shows an example of a text mining approachaccording to the invention.)

The model is developed using a supervised machine learning approach thatlearns from tagged data. The model is then used to classify new data.Thus, the data is initially tagged by human operators to build a model.Thereafter, the new data is applied to the model to produce a predictedoutput. The accuracy of the output first produced by the text miningoperation is validated (206) and, once validation is acceptable, furthervalidation is performed, for example by comparing against tagged data(208). As the system learns, the algorithm used for text mining withregard to the issue of concern is modified (210). Thereafter, thedataset is extracted and a final data model is built (214).

Once the final data model is built, new data is applied to the system.The system then divides the time components in the new data, i.e.pre-chat, actual chat, and post-chat, between agent time and customertime (216). A resulting dataset is produced (218) containing suchinformation as, for example, customer ID, main issue, one or moresub-issues, agent/customer time for the main issue, agent/customer timefor the sub-issues, transfer type, transfer time, hold time, pre-chattime, post-chat time, number of customer lines, number of agent lines,number of lines of separation, and status concerning disconnections bycustomer chats.

One aspect of the invention provides reporting for various experimentson the predictive service platform. For example, a software engine isprovided that uses the customer's Web journey data, as well as CRM data,to target customers, evaluate their intent and the right time and modeof engagement to fulfill that intent. This embodiment also provides aprogram that can take decisions to engage with a customer in thecustomer's Web journey.

FIGS. 13a and 13b are a flow diagrams showing an algorithm forextraction of a primary question during a chat session (FIG. 13a ) andextracting an issue (FIG. 13b ) according to the invention.

In FIG. 13a , the first question in a chat session is identified (300).(See, also, FIGS. 9 and 10 which show two possible variations in amultitude of possible variations in identifying the primary issue line.These are all heuristic based approaches to identifying a primary line.)

The position of the question in the chat transcript is then identified(302). In the presently preferred embodiment of the invention, if theposition of the question in the transcript is greater than or equal tothree (306), then the issue is extracted (305); else, the transcript isanalyzed to determine if a phrase such as “May I help you?” or “May Iassist you?” is present (306). If such a phrase is found, then the nextsentence is deemed to be the key question (308); else, the systemidentifies the first customer sentence in the transcript that has atleast four words as the key question (310). Those skilled in the artwill appreciate that the position of the sentence in the transcript, thephrases identified, and the number of words in the first customersentence are a matter of choice when implementing the invention and thatother values and variations on the steps set forth in connection withFIG. 13a may be used and are considered to be within the scope of theinvention.

In FIG. 13b , the primary question has been located and the issue isthen extracted. The system extracts a list of FAQs with regard to thequestion and having related answers (320). The system then extractsunigrams and bigrams from the question (322). An n-gram is a subsequenceof n items from a given sequence. The items in question can be phonemes,syllables, letters, words, or base pairs according to the application.An n-gram of size 1 is referred to as a unigram; size 2 is a bigram;etc. An n-gram model is one type of probabilistic model that can be usedin an embodiment of the invention for predicting the next item in asequence. The n-grams are extracted using classic text mining techniquesfor feature extraction. A list of top unigrams and bigrams is preparedbased upon their occurrence in customer issues (324) based on typicalcriteria used in text mining, such as TFIDF. See, for example, Sholom MWeiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau, TextMining—Predictive Methods for Analyzing Unstructured Information,Springer Publications. The bigrams are mapped with customer issues (326)based on a probabilistic model, where the probability of a certainn-gram belonging to a certain issue category is determined based onfeatures that have historically been seen in chats discussing that issuecategory. Such mapping of features to issue category is performed in oneembodiment using manual tagging. The unigrams and bigrams are thenmapped back to the query categories (328). If there is a match, then theissue has been successfully extracted; else, a match is made to theunigram to obtain the issue (330).

Problem Predictor

One embodiment provides a system that self-learns based on previouscustomer interactions and their performance metrics. For example, aprogram provides a templatized widget framework to enable an overlaywidget, with rich content, on a client's Website to assist customers ina self-service mode.

FIG. 14 shows a sample progress table for a problem predictor accordingto the invention. In FIG. 14, variables evaluated include journeyvariables, source destination variables, chat time to departure/arrivaltime, code sharing or non-code sharing variables, time of day, andgeography variables. See, also, FIGS. 7 and 8.

In a presently preferred embodiment of the invention, problem predictionis effected as a product of probabilities p for a question Q with regardto a plurality of answers A, as shown in Equation (1) below.p(Q/A ₁ , . . . ,A _(n))=p(Q)p(A1/Q)p(A2/Q) . . . p(A _(n) /Q)  (1)

This is a classic Naïve Bayes algorithm. Other algorithms that canpredict the probability of the response variable, in this case an issueprediction, based on various customer attributes can be used and theinvention is not limited to the use of a Naïve Bayes algorithm.

Rules Engine

One aspect of the invention provides plug-and-play support for variousanalytical algorithms and complex multivariate models in differentimplementations. Examples of such algorithms include Naïve Bayes,Logistic regression, Support Vector Machines, etc. There could be manyother algorithms as well. Such system enables distributing conditionevaluations across a client, i.e. browser, and server for optimizingperformance. A database is provided that can hold multiple versions ofcomplex rule configurations, and that can be taken live without anydowntime.

Each published change of rule engine configuration is associated with aversion specific to a client. Typically, the latest two versions aremaintained to allow the possibility of switching back to a particularversion at any point in time.

A particular version is marked as the active or default version(typically the latest one), and this version of the configuration ispicked up for all visitors of the website. However, for experimentationand testing purposes, there is a possibility to map requests coming infrom a set of IPs to a particular version or randomly select a versionbased on a probability distribution.

While configuring a new version, the new version becomes available forgeneral use only when marked as the active version and any existingversion is not impacted because of the change. In effect, there is nodowntime incurred for the system because the publishing and switching ofversions are done atomically. A published version can be checked forquality extensively before it is used in production, thereby improvingthe reliability of the system.

An aspect of this embodiment is a program that generates events duringthe lifecycle of a customer's session, and that allows other systemcomponents to subscribe for, and take appropriate actions.

FIG. 15 is a flow diagram that shows a PSP process in the datacollection and rule evaluation stages according to the invention. Suchsystem supports segmentation and experimentation to experiment onsolutions for a best customer experience and includes, but is notlimited to, performance of analytical models, as well as engagementexperience, e.g. UI and interaction flow, with the customer data-drivendecision making in a Web application, where a decision is based onclient side data, as well as backend, data.

Key features of this embodiment of the invention include a rules enginethat divides the entire decision making into three phases: client sidedata collection and client side condition evaluation; server side datacollection from third party integrations (DB, CRM, etc.); and serverside condition evaluation based on client, as well as server side, data.

Client side data includes, but is not limited to, time on a page,cookies, DOM data, HTML5 storage, and data obtained anywhere during aWeb journey.

Server side data includes, but is not limited to, a page visitor'sprofile, past history and any third party data coming from a backend,for example, geographic information, weather information, etc.

Rules are defined in different contexts. For example, in a context,rules are inter-related in the following manner:

-   -   One fired rule can suppress other rules for a configurable        amount of time;    -   One fired rule suppresses other rules for the lifetime of the        visitor, expiry of which is configurable in the framework; and    -   One fired rule can change other rules' priorities in specific        context or for the lifetime of the visitor.

The rules engine can be added to any existing portal by including asimple script tag which loads the required JavaScript either at thebottom of the including page or in its head tag.

In an embodiment, some decision making is delegated on the client sideto scale the decision making system. All the complex rules are hidden onthe server side and can be managed by a configuration UI without anyinvolvement of the portal owner on which rule engine is tagged.

Rules can be combined in a group for categorization and rule management.Rules can interact with each other in the system, such that the outcomeof a rule can enable/disable certain other set of rules or suppress themfor the given visitor for certain period of time or forever. Rules areevaluated on the server side on highly scalable servers and, hence, canbe very complex based on multivariate models and scoring of differentmathematical functions. Rules that are executed on the server can alsotake benefit of prediction algorithms to extrapolate outcomes given aset of parameters.

The rules engine supports multiple versions of the rules configurationand it is possible to maintain and push one of them live anytime fromthe configuration console. The rules engine decides on the target andcontrol group based on criteria defined on the backend. It is possibleto configure multiple control groups and test different rules on each ofthem. The rules engine can generate multiple events that other consumerscan subscribe for further processing. FIG. 15 is a flow diagram thatshows a PSP process in the data collection and rule evaluation stagesaccording to the invention.

Client Storage

Today, all popular browsers support multiple tab or multiple windowbrowsing and users do use multiple tabs to browse the Internet. It mayhappen that a user opens the same Website in multiple tabs at the sametime. There is a possibility that a Website, when opened in a browser,needs to store data in user's computer storage to function properly.Thus, it is clear that a Website may need to store data on the user'smachine and that, too, across multiple tabs or windows. Also, most ofthe popular browsers, such as Mozilla Firefox, Internet Explorer, andSafari, do not provide any synchronization construct for Websites to beable to access/write data in user's computer storage from multiplewindows/tabs in a consistent and safe manner.

One feature of the invention provides a system that takes advantage ofthe persistent local storage on the browser to keep track of all theinformation through out the user's journey, thereby making the serverstateless. In an embodiment, a program guides the customer through aresolution path on the Website using a step-by-step widget, that cantrack the exact state of the user. Such program can be a module orscript that runs in browsers and provides synchronization construct formultiple tabs/browsers, thus opening up the same Website to accesscomputer storage in a consistent manner using, for example, browserevents and JavaScript.

Thus, this embodiment comprises a solution which provides any Websitewith a consistent and safe way to read and write to the user's computerstorage from multiple tabs or windows. The implementation approach isgeneric and any Website can use it.

On a high level, the system achieves read/write synchronization on theuser's computer storage by dividing the storage into different areas andusing the browser's JavaScript events. There are algorithms which useJavaScript events generated by the browser and the storage itself toprovide the above mentioned synchronization and a way to store the datainto the user's computer storage from multiple windows and tabs.

The following events are examples of events that are used in theinvention: focus event, blur event, mouse over event, unload event, andonload event. The system creates the following two logical storage areasto store all the data:

-   -   Shared Storage area: which is common for the Website and for all        tabs and windows and is accessed, read and write, from all the        windows; and    -   Window storage area: which every window has its own copy of.        This area is also accessed, read only, from all the windows.

A locking mechanism is used to provide a given window the right to reador write into the shared storage area. Thus, at a given point of time,only the window that has this lock and can read/write into the sharedstorage area. The locking mechanism is implemented in this embodimentusing a combination of focus, blur, and mouse over events generated bythe browser; and lock information, including timestamp, that ismaintained inside a window storage area of each window. The events, i.e.focus, blur, mouse over, are used to switch the lock from one window toanother. The lock information stored in the window storage areadetermines which window currently has the lock. Also, the read access tothe window storage area of all the windows from a given window is suchthat it ensures fault tolerance in the case of concurrent multiple readsand a single write.

FIG. 16 is a timeline that shows a visitor session life cycle accordingto the invention.

FIG. 17 is a block schematic diagram that shows periodic, on-demand useractivity information tracking according to the invention.

FIG. 18 is a block schematic diagram that shows the multiple tab windowsynchronization problem.

FIG. 19 is a block schematic diagram that shows a client sideinformation storage system according to the invention.

FIG. 20 is a flow diagram that shows a multiple tab synchronizationalgorithm according to the invention.

Experimentation Framework

An interaction system can be composed of several building blocks whichtogether serve a common purpose, i.e. interact with the end user in thebest possible manner.

These building blocks can be:

-   -   “Rules” which trigger a new interaction flow or an action within        the interaction.    -   The channel of the interaction, for example Web, phone, self        service, chat, voice, etc.    -   The experience within a given channel.    -   The seamless handling of an interaction across various channels.

To optimize these building blocks to achieve the best business outcomes,one needs to experiment with these building blocks and determine whichconfiguration of these building blocks produces the optimum results in areal world environment. The optimization can be on any parameter or setof parameters, based on the business needs.

Here are a few example use cases:

-   -   1. The ability to evaluate different set of rules for different        samples of visitors in real time. This experimentation can help        figure out which set of rules performs the best.    -   2. The ability to take different actions whenever a rule        triggers for different samples of visitors in real time. This        experimentation can help figure out which action performs the        best.    -   3. The ability to have different working, content, and look and        feel of an action for different samples of visitors in real        time. This again, can help figure out which content/working/look        and feel leads to best results.

While experimentation is an utmost need in today's fast changing world,the cost of a failure in an experiment needs to be kept low.Experimentation should happen through a pre-configured system which canensure predictability. Experimentation should be possible in real timeand in the real world (live) environment. It should be possible to tryout different configurations within any building block.

Experimentables

An experimentable is an entity that someone may like to experiment with.This means the same entity may have different values for differentsamples of users. The intention is to find out which value for thatentity gives optimum results.

Examples

-   -   Set of rules to be evaluated on website visitors. In this case,        different setS of rules ARe evaluated for different samples of        visitors to see which set works best.    -   Widget. In this case, different widgets can be shown to the        different samples of users.    -   Widget Experience. In this case, a different widget experience,        including content, flow, color theme, etc. is shown to different        samples of visitors.        Experimentable Values

There are various values an experimentable may take depending on thesituation, these values are termed as experimentable values.

For example:

-   -   A “widgetColor” experimentable may take {Red, Green, Blue}        values for different samples of visitors.    -   A “widgetWelcomeMessage” experimentable may take {“Hello”, “Hi”}        values for different samples of visitors.

Experimentable Rules

For the purpose of experimentation, every experimentable can beconfigured to have many different values. Which value should be appliedfor a given visitor can be dependent on various factors. Differentvalues need to be applied under different situations.

To articulate the mapping of situations to set of values possible inthat situation, the experimentation framework provides the concept ofexperimentable rules.

An experimentable rule is a situation under which a given set ofexperimentable values should be considered. These rules are totallyflexible can be based on any number, kind of conditions. The variablesused in these conditions again can come from any source, including anonline real time parameter, an offline pre-configured parameter, and aderived parameter which is a combination of the above. These rules areevaluated in the configured order of priority. If a rule is satisfied,the rest of the (lower priority) rules are not evaluated.

For example:

-   -   Lets say we want to configure the experimentable—“WidgetColor”.    -   We want one of {Red, Green, Blue} for the visitors which satisfy        a situation ‘x’.    -   And we want one of {violet, pink} for the visitors which satisfy        the situation ‘y’.    -   In such a case we should configure two experiment rules ‘x’ and        ‘y’.    -   ‘x’ and ‘y’ should be composed of the conditions which define        the situations ‘x’ and ‘y’ respectively.        Experimentable Value Selection Policy

Every experimentable rule points to a set of values which should beconsidered for a given visitor. The selection of the value within theconsidered set depends on a pre-configured policy. This is termed asexperimentable value selection policy. Any policy can be applied tochoose between the set of values.

For example:

-   -   Lets say we want to configure the experimentable—“WidgetColor”.    -   We want one of {Red, Green, Blue} for the visitors which satisfy        the experiment rule ‘x’.    -   The selection policy could be as simple as “RoundRobin”.    -   In such a case, Red, Green and blue will be selected in a round        robin fashion.        Experimentation Tags

An experimentation tag is a string key which can have any one of a setof values. The selection of the experimentation tag value is donewhenever the given tag is assigned to a new visitor. The experimentationtag value determines the sample into which this visitor falls into.Every user can be assigned multiple experimentation tags, and theselected value with each tag, during various stages of a session. Thevalue of a given tag for a visitor is determined based on a randomnumber sampling. Also, experimentation tags are persistent. This meansthat once an experiment tag is created it can be used across allexperimentables for a given visitor.

Experimentation Tag Values:

An experimentation tag value is a string which helps in identifying aparticular kind of output for a given experimentable.

For example:

-   -   WidgetTag: {VisitorsSampleForWidgets1,        VisitorsSampleForWidgets2}. These are the two string values of        the tag with the key ‘WidgetTag’.    -   GeneralTag: {generalVisitorSample1,        generalVisitorSample2,generalVisitorSample3}. These are the        three possible string values of the tag with the key        ‘GeneralTag’.

Example experimentation tag value distributions:

-   -   WidgetTag: 10% visitors—VisitorsSampleForWidgets1, 90%        visitors—VisitorsSampleForWidgets1.

Out of all the visitors which are assigned the ‘widgetTag’, 10% wouldhave the value ‘VisitorsSampleForWidgets1’ and the rest would have‘VisitorsSampleForWidgets2’.

-   -   GeneralTag: 5% visitors generalVisitorSample1, 80%        visitors—generalVisitorSample2, 15%        visitors—generalVisitorSample3

Out of all the visitors which are assigned the ‘GeneralTag’, 5% wouldhave the value ‘generalVisitorSample1’, 80% would have the value‘generalVisitorSample2 and the rest would have the value‘generalVisitorSample2’.

Given the above two tags, the visitors are sampled across two dimensionsand 6(2×3) samples are created (see Table 1 below).

TABLE 1 General Tag(5%) General Tag(80%) General Tag(15%) WidgetTagsample 1-10% of sample 2-10% of sample 3-10% of (10) 5% 80% 15%WidgetTag sample 4-90% of sample 5-90% of sample 6-90% of (90) 5% 80%15%

Table 2 below provides an example scenario where each visitor isassigned two experimentation tags and the values are assigned based onrandom number sampling.

TABLE 2 WidgetTag (Values GeneralTag (Values Visitors below) below)visitor1-John VisitorsSampleForWidgets1 generalVisitorSample2visitor2-Jane VisitorsSampleForWidgets2 generalVisitorSample1visitor3-unidentified VisitorsSampleForWidgets2 generalVisitorSample3Design

FIG. 21 is a block schematic diagram that shows a high level design of areal time experimentation framework according to the invention.

FIG. 22 is a flow diagram that show visitor tagging according to theinvention.

FIG. 23 is a flow diagram that shows real time experimentable valuedetermination according to the invention.

Features

-   -   1. The cost of a failure for experiment needs can be kept low by        keeping the experiment tag value percentage configurations.    -   2. Experimentation happens through a pre-configured system,        including experiment tags, rules, values, and the selection        policy.    -   3. Experimentation is supported in real time and in the real        world (live) environment. Every visitor's end-to-end experience        can be experimented with given the above system.    -   4. It is possible to experiment the following building blocks of        an interaction management system:        -   a. The rules that should be evaluated on a given visitor.        -   b. The engagement channel that should be selected for a            given visitor.        -   c. The engagement experience within the given channel.    -   5. Also, it is possible to create independent visitor samples at        different stages of the interaction using experimentation tags.    -   6. An experimentation tag is persistent so that the same tag can        be used across all the various experimentables for a given a        visitor interaction experience.    -   7. Experimentable values can be chosen based on the following        two constructs:        -   a. Experimentable rules; and        -   b. Value selection policy.    -   8. Every experimentable rule can be based on the following        inputs parameters:        -   a. An online real time parameter including system            parameters, such as experiment tags, etc.; and including            visitor parameters, such as Website variables, etc.;        -   b. An offline pre-configured parameter; and        -   c. A derived parameter which is a combination of the above.    -   9. Experimentable value selection is a based on a policy and can        be configured separately.    -   10. Value selection policy is de-coupled from the experiment        rule evaluation (situation determination).        Computer Implementation

FIG. 24 is a block schematic diagram of a machine in the exemplary formof a computer system 1600 within which a set of instructions for causingthe machine to perform any one of the foregoing methodologies may beexecuted. In alternative embodiments, the machine may comprise orinclude a network router, a network switch, a network bridge, personaldigital assistant (PDA), a cellular telephone, a Web appliance or anymachine capable of executing or transmitting a sequence of instructionsthat specify actions to be taken.

The computer system 1600 includes a processor 1602, a main memory 1604and a static memory 1606, which communicate with each other via a bus1608. The computer system 1600 may further include a display unit 1610,for example, a liquid crystal display (LCD) or a cathode ray tube (CRT).The computer system 1600 also includes an alphanumeric input device1612, for example, a keyboard; a cursor control device 1614, forexample, a mouse; a disk drive unit 1616, a signal generation device1618, for example, a speaker, and a network interface device 1628.

The disk drive unit 1616 includes a machine-readable medium 1624 onwhich is stored a set of executable instructions, i.e., software, 1626embodying any one, or all, of the methodologies described herein below.The software 1626 is also shown to reside, completely or at leastpartially, within the main memory 1604 and/or within the processor 1602.The software 1626 may further be transmitted or received over a network1630 by means of a network interface device 1628.

In contrast to the system 1600 discussed above, a different embodimentuses logic circuitry instead of computer-executed instructions toimplement processing entities. Depending upon the particularrequirements of the application in the areas of speed, expense, toolingcosts, and the like, this logic may be implemented by constructing anapplication-specific integrated circuit (ASIC) having thousands of tinyintegrated transistors. Such an ASIC may be implemented withcomplementary metal oxide semiconductor (CMOS), transistor-transistorlogic (TTL), very large systems integration (VLSI), or another suitableconstruction. Other alternatives include a digital signal processingchip (DSP), discrete circuitry (such as resistors, capacitors, diodes,inductors, and transistors), field programmable gate array (FPGA),programmable logic array (PLA), programmable logic device (PLD), and thelike.

It is to be understood that embodiments may be used as or to supportsoftware programs or software modules executed upon some form ofprocessing core (such as the CPU of a computer) or otherwise implementedor realized upon or within a machine or computer readable medium. Amachine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine, e.g. acomputer. For example, a machine readable medium includes read-onlymemory (ROM); random access memory (RAM); magnetic disk storage media;optical storage media; flash memory devices; electrical, optical,acoustical or other form of propagated signals, for example, carrierwaves, infrared signals, digital signals, etc.; or any other type ofmedia suitable for storing or transmitting information.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the Claims includedbelow.

The invention claimed is:
 1. A computer implemented method for problemprediction in a customer service environment, said method performed by adata processor and comprising: using, by said data processor, a smartchat routing module to divide chat related information into pre-chat,actual chat, and post chat; receiving, by said data processor, a chattranscript from said smart chat routing module and identifying aposition in the chat transcript at which an issue of concern is posed bya customer; using, by said data processor, an algorithm for text miningthe chat transcript for key lines that identify the issue of concern;using, by said data processor, a problem predictor to apply a data modelto produce a predicted output for bootstrapping guided self-service,where problem prediction is effected as a product of probabilities p fora question Q with regard to a plurality of answers A; based upon saidpredicted output, said problem predictor triggering a templatized widgetframework to enable an overlay of a widget on a Website presented on abrowser running on a computer of said customer to implement a guidedself-service mode; making, by said data processor, a problem predictionserver stateless by using persistent local storage for said browser tokeep track of information of said customer's journey; guiding, by saiddata processor, said customer through a resolution path on said Websitein a step-by-step manner with said widget that tracks a state of saidcustomer, providing, by said data processor, read/write synchronizationby dividing said local storage into different areas and using saidbrowser's JavaScript events; and creating, by said data processor, twological storage areas of said local storage to store event data, whereinsaid logical storage areas include: a shared storage area that is commonfor said Website and multiple windows of computer applications and whichis configured to be accessed for read and write operations by saidwindows, and a window storage area of which every window has a copy andwhich is configured to be accessed for read only operations by saidmultiple windows.
 2. The method of claim 1, further comprising; creatingsaid data model using a supervised machine learning approach that learnsfrom tagged data, wherein data is initially tagged by human operators tobuild a model; using said data model to classify new data and applyingthe new data to the data model to produce said predicted output;validating accuracy of said predicted output first produced by textmining and, once validation is acceptable, performing further validationby comparing said predicted output against tagged data; dynamicallymodifying the algorithm used for text mining with regard to an issue ofconcern as the data model learns; and extracting a dataset and buildinga final data model.
 3. The method of claim 2, further comprising: oncethe final data model is built, applying the new data and dividing timecomponents in the new data into pre-chat, actual chat, and post chat,and between agent time and customer time; and producing a resultingdataset containing information comprising any of customer ID, mainissue, one or more sub-issues, agent/customer time for the main issue,agent/customer time for the sub-issues, transfer type, transfer time,hold time, pre-chat time, post-chat time, number of customer lines,number of agent lines, number of lines of separation, and statusconcerning disconnections by customer chats.
 4. The method of claim 3,further comprising: basing said data model self-learning on previouscustomer interactions and their performance metrics; wherein variablesevaluated in connection with said customer interactions comprise any ofjourney variables, source destination variables, chat time todeparture/arrival time, code sharing or non-code sharing variables, timeof day, and geography variables.
 5. The method of claim 2, furthercomprising: providing plug-and-play support for a plurality ofanalytical algorithms and complex multivariate models in differentimplementations; wherein said algorithms comprise any of Naive Bayes,Logistic regression, and Support Vector Machines.
 6. The method of claim5, further comprising: distributing condition evaluations across aclient and server to optimize performance using a database that holdsmultiple versions of complex rule configurations and that can be takenlive without any downtime; associating each of a plurality of publishedchange of rule engine configurations with a version specific to aclient; maintaining at least the latest two versions to allow switchingback to a particular version at any point in time; and marking aparticular version as an active or default version that is picked up forall visitors of a Website.
 7. The method of claim 6, further comprising:using a probability distribution for mapping requests from a set of IPsto a particular version or randomly selected version; while configuringa new version, making the new version available for general use onlywhen marked as the active version without impacting any existing versionbecause of the change; performing publishing and switching of versionsatomically with no downtime incurred; and checking a published versionfor quality extensively before it is used in production.
 8. The methodof claim 1, further comprising: extracting a primary question during achat session by identifying a first question in said chat transcript andidentifying a position of the question in the chat transcript;extracting the issue if the position of the question in the chattranscript is greater than or equal to a predetermined value; else,analyzing the chat transcript to determine if a predetermined phrase ispresent; deeming a next sentence to be the primary question if saidpredetermined phrase if found; else, identifying a first customersentence in the chat transcript that has a predetermined number of wordsas the primary question; after the primary question has been extracted,extracting an issue by extracting a list of text-based materials withregard to the primary question, said list comprising one or more relatedanswers; extracting unigrams and bigrams from the primary question;preparing a list of top unigrams and bigrams based upon their occurrencein customer issues; mapping said bigrams with said customer issues;mapping said unigrams and bigrams back to one or more query categories;and determining that the issue has been successfully extracted if thereis a match, else, making a match to the unigram to obtain the customerissue.
 9. The method of claim 1, wherein: said widget running in thebrowser and providing a synchronization construct for multipletabs/browsers to open up a same Website to access system storage in aconsistent manner using any of browser events and JavaScript; saidWebsite is provided with a consistent and safe way to read and write tothe local storage from the multiple windows.
 10. The method of claim 9,wherein said browser events comprise any of a focus event, blur event,mouse over event, unload event, and onload event.
 11. The method ofclaim 10, further comprising: a locking mechanism for providing a givenwindow the right to read or write into the shared storage area; whereinat a given point of time, only the window that has this lock canread/write into the shared storage area; using a combination of focus,blur, and mouse over events generated by the browser to implement thelocking mechanism; maintaining lock information, including a timestamp,inside a window storage area of each window; using browser eventscomprising any of focus, blur, and mouse over to switch the lock fromone window to another; and using lock information stored in the windowstorage area to determine which window currently has the lock; whereinread access to the window storage area of the multiple windows from agiven window ensures fault tolerance in case of concurrent multiplereads and a single write.
 12. The method of claim 1, further comprising:generating real time triggers based upon one or more inputs, said realtime triggers comprising any of agent alerts and supervisor alerts;wherein said one or more inputs comprise any of an off line average holdtime (AHT) reduction solution and values for customer pre-chat time,issue time, and post-chat time.